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Copyright © 2022 Malliga Subramanian et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/

Abstract

Retinal abnormalities have emerged as a serious public health concern in recent years and can manifest gradually and without warning. These diseases can affect any part of the retina, causing vision impairment and indeed blindness in extreme cases. This necessitates the development of automated approaches to detect retinal diseases more precisely and, preferably, earlier. In this paper, we examine transfer learning of pretrained convolutional neural network (CNN) and then transfer it to detect retinal problems from Optical Coherence Tomography (OCT) images. In this study, pretrained CNN models, namely, VGG16, DenseNet201, InceptionV3, and Xception, are used to classify seven different retinal diseases from a dataset of images with and without retinal diseases. In addition, to choose optimum values for hyperparameters, Bayesian optimization is applied, and image augmentation is used to increase the generalization capabilities of the developed models. This research also provides a comparison of the proposed models as well as an analysis of them. The accuracy achieved using DenseNet201 on the Retinal OCT Image dataset is more than 99% and offers a good level of accuracy in classifying retinal diseases compared to other approaches, which only detect a small number of retinal diseases.

Details

Title
Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images
Author
Subramanian, Malliga 1 ; Kumar, M Sandeep 2 ; Sathishkumar, V E 3 ; Jayagopal Prabhu 2   VIAFID ORCID Logo  ; Alagar Karthick 4 ; Ganesh, S Sankar 5   VIAFID ORCID Logo  ; Mahseena Akter Meem 6   VIAFID ORCID Logo 

 Department of Computer Science Engineering, Kongu Engineering College, Perundurai, Erode 638060, Tamil Nadu, India 
 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India 
 Department of Industrial Engineering, Hanyang University, Seoul, Republic of Korea 
 Renewable Energy Lab, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamil Nadu, India 
 Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore 641407, Tamil Nadu, India 
 Department of Electrical and Electronic Engineering, Daffodil International University, Ashulia, Savar, Dhaka 1207, Bangladesh 
Editor
Ripon Chakrabortty
Publication year
2022
Publication date
2022
Publisher
John Wiley & Sons, Inc.
ISSN
16875265
e-ISSN
16875273
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2653898957
Copyright
Copyright © 2022 Malliga Subramanian et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/